Overview

Dataset statistics

Number of variables9
Number of observations17472
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory80.0 B

Variable types

DateTime1
TimeSeries8

Timeseries statistics

Number of series8
Time series length17472
Starting point2022-01-01 00:00:00
Ending point2023-12-30 23:00:00
Period1 hour and 5.89 seconds
2024-05-09T10:10:33.302429image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:34.435790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Alerts

dewpt is highly overall correlated with tempHigh correlation
dhi is highly overall correlated with dni and 1 other fieldsHigh correlation
dni is highly overall correlated with dhi and 1 other fieldsHigh correlation
ghi is highly overall correlated with dhi and 1 other fieldsHigh correlation
temp is highly overall correlated with dewptHigh correlation
temp is non stationaryNon stationary
wind_spd is non stationaryNon stationary
dhi is non stationaryNon stationary
ghi is non stationaryNon stationary
dni is non stationaryNon stationary
clouds is non stationaryNon stationary
dewpt is non stationaryNon stationary
rh is non stationaryNon stationary
temp is seasonalSeasonal
wind_spd is seasonalSeasonal
dhi is seasonalSeasonal
ghi is seasonalSeasonal
dni is seasonalSeasonal
clouds is seasonalSeasonal
dewpt is seasonalSeasonal
rh is seasonalSeasonal
dhi has 8632 (49.4%) zerosZeros
ghi has 8672 (49.6%) zerosZeros
dni has 8654 (49.5%) zerosZeros
clouds has 1036 (5.9%) zerosZeros

Reproduction

Analysis started2024-05-09 08:10:10.402371
Analysis finished2024-05-09 08:10:32.477693
Duration22.08 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Distinct17470
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size273.0 KiB
Minimum2022-01-01 00:00:00
Maximum2023-12-30 23:00:00
2024-05-09T10:10:35.187709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:35.351550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

temp
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct322
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.098094
Minimum-0.5
Maximum34.7
Zeros0
Zeros (%)0.0%
Memory size273.0 KiB
2024-05-09T10:10:35.725022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.5
5-th percentile6.7
Q112.7
median18
Q324.025
95-th percentile28.8
Maximum34.7
Range35.2
Interquartile range (IQR)11.325

Descriptive statistics

Standard deviation6.9540729
Coefficient of variation (CV)0.38424338
Kurtosis-0.9045947
Mean18.098094
Median Absolute Deviation (MAD)5.6
Skewness-0.078815103
Sum316209.9
Variance48.35913
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.1805122332
2024-05-09T10:10:35.989127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-05-09T10:10:36.899701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2024-05-09T10:10:37.067818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
14.7 217
 
1.2%
15 196
 
1.1%
26 194
 
1.1%
12.2 190
 
1.1%
14.2 188
 
1.1%
14 178
 
1.0%
13.2 176
 
1.0%
14.5 172
 
1.0%
15.5 170
 
1.0%
12.5 168
 
1.0%
Other values (312) 15623
89.4%
ValueCountFrequency (%)
-0.5 2
 
< 0.1%
-0.2 1
 
< 0.1%
0.1 1
 
< 0.1%
0.2 1
 
< 0.1%
0.5 5
< 0.1%
0.7 1
 
< 0.1%
1 3
< 0.1%
1.1 1
 
< 0.1%
1.5 2
 
< 0.1%
1.6 4
< 0.1%
ValueCountFrequency (%)
34.7 2
< 0.1%
34.4 1
 
< 0.1%
34.2 1
 
< 0.1%
34 1
 
< 0.1%
33.8 1
 
< 0.1%
33.7 2
< 0.1%
33.6 3
< 0.1%
33.5 1
 
< 0.1%
33.4 1
 
< 0.1%
33.1 2
< 0.1%
2024-05-09T10:10:36.566632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

wind_spd
Numeric time series

NON STATIONARY  SEASONAL 

Distinct133
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1028028
Minimum0
Maximum15.9
Zeros1
Zeros (%)< 0.1%
Memory size273.0 KiB
2024-05-09T10:10:37.369495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.6
median3.6
Q35.09
95-th percentile8.19
Maximum15.9
Range15.9
Interquartile range (IQR)2.49

Descriptive statistics

Standard deviation2.1834863
Coefficient of variation (CV)0.53219382
Kurtosis1.8332907
Mean4.1028028
Median Absolute Deviation (MAD)1
Skewness1.0922296
Sum71684.17
Variance4.7676124
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.78182106 × 10-23
2024-05-09T10:10:37.627609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-05-09T10:10:38.469595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2024-05-09T10:10:38.685808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3.6 2001
11.5%
3.1 1935
11.1%
4.09 1751
10.0%
2.6 1561
8.9%
4.59 1442
8.3%
2.1 1384
 
7.9%
5.09 1214
 
6.9%
1 935
 
5.4%
6.2 851
 
4.9%
1.5 847
 
4.8%
Other values (123) 3551
20.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.25 1
 
< 0.1%
0.33 1
 
< 0.1%
0.4 12
 
0.1%
0.5 203
 
1.2%
0.72 1
 
< 0.1%
0.75 1
 
< 0.1%
0.8 13
 
0.1%
0.87 1
 
< 0.1%
1 935
5.4%
ValueCountFrequency (%)
15.9 1
 
< 0.1%
15.4 2
 
< 0.1%
14.4 8
 
< 0.1%
13.9 9
 
0.1%
13.4 23
0.1%
12.9 25
0.1%
12.4 3
 
< 0.1%
12.3 32
0.2%
11.85 1
 
< 0.1%
11.8 31
0.2%
2024-05-09T10:10:38.091031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

dhi
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct124
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.858288
Minimum0
Maximum123
Zeros8632
Zeros (%)49.4%
Memory size273.0 KiB
2024-05-09T10:10:39.030384image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8.5
Q391
95-th percentile118
Maximum123
Range123
Interquartile range (IQR)91

Descriptive statistics

Standard deviation47.37767
Coefficient of variation (CV)1.1054494
Kurtosis-1.5564483
Mean42.858288
Median Absolute Deviation (MAD)8.5
Skewness0.41366257
Sum748820
Variance2244.6436
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9044002042
2024-05-09T10:10:39.277280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-05-09T10:10:40.157641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2024-05-09T10:10:40.372481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 8632
49.4%
122 232
 
1.3%
117 218
 
1.2%
121 216
 
1.2%
109 170
 
1.0%
118 164
 
0.9%
110 162
 
0.9%
116 156
 
0.9%
99 156
 
0.9%
112 152
 
0.9%
Other values (114) 7214
41.3%
ValueCountFrequency (%)
0 8632
49.4%
1 12
 
0.1%
2 12
 
0.1%
3 12
 
0.1%
4 18
 
0.1%
5 8
 
< 0.1%
6 18
 
0.1%
7 10
 
0.1%
8 14
 
0.1%
9 20
 
0.1%
ValueCountFrequency (%)
123 140
0.8%
122 232
1.3%
121 216
1.2%
120 120
0.7%
119 136
0.8%
118 164
0.9%
117 218
1.2%
116 156
0.9%
115 124
0.7%
114 110
0.6%
2024-05-09T10:10:39.732126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

ghi
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct983
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean232.08883
Minimum0
Maximum991
Zeros8672
Zeros (%)49.6%
Memory size273.0 KiB
2024-05-09T10:10:40.717410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q3437
95-th percentile878
Maximum991
Range991
Interquartile range (IQR)437

Descriptive statistics

Standard deviation308.20304
Coefficient of variation (CV)1.3279529
Kurtosis-0.33968156
Mean232.08883
Median Absolute Deviation (MAD)3
Skewness1.0316001
Sum4055056
Variance94989.115
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.6957092143
2024-05-09T10:10:41.118548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-05-09T10:10:43.922284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2024-05-09T10:10:44.136027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 8672
49.6%
64 32
 
0.2%
370 32
 
0.2%
894 30
 
0.2%
1 30
 
0.2%
869 28
 
0.2%
191 28
 
0.2%
2 28
 
0.2%
770 28
 
0.2%
991 26
 
0.1%
Other values (973) 8538
48.9%
ValueCountFrequency (%)
0 8672
49.6%
1 30
 
0.2%
2 28
 
0.2%
3 24
 
0.1%
4 12
 
0.1%
5 16
 
0.1%
6 18
 
0.1%
7 18
 
0.1%
8 10
 
0.1%
9 16
 
0.1%
ValueCountFrequency (%)
991 26
0.1%
990 14
0.1%
989 10
 
0.1%
988 8
 
< 0.1%
987 6
 
< 0.1%
986 6
 
< 0.1%
985 6
 
< 0.1%
984 6
 
< 0.1%
983 4
 
< 0.1%
982 4
 
< 0.1%
2024-05-09T10:10:43.475969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

dni
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct805
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean349.05357
Minimum0
Maximum924
Zeros8654
Zeros (%)49.5%
Memory size273.0 KiB
2024-05-09T10:10:44.472194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median26.5
Q3767
95-th percentile903
Maximum924
Range924
Interquartile range (IQR)767

Descriptive statistics

Standard deviation380.44115
Coefficient of variation (CV)1.0899219
Kurtosis-1.7101447
Mean349.05357
Median Absolute Deviation (MAD)26.5
Skewness0.32548006
Sum6098664
Variance144735.47
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9073866562
2024-05-09T10:10:44.720649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-05-09T10:10:45.470033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2024-05-09T10:10:45.687415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 8654
49.5%
893 98
 
0.6%
915 88
 
0.5%
914 80
 
0.5%
857 62
 
0.4%
920 62
 
0.4%
870 60
 
0.3%
913 54
 
0.3%
919 54
 
0.3%
892 52
 
0.3%
Other values (795) 8208
47.0%
ValueCountFrequency (%)
0 8654
49.5%
1 10
 
0.1%
2 4
 
< 0.1%
3 8
 
< 0.1%
5 12
 
0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
11 2
 
< 0.1%
ValueCountFrequency (%)
924 38
0.2%
923 42
0.2%
922 28
 
0.2%
921 24
 
0.1%
920 62
0.4%
919 54
0.3%
918 40
0.2%
917 38
0.2%
916 30
 
0.2%
915 88
0.5%
2024-05-09T10:10:45.121081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

clouds
Numeric time series

NON STATIONARY  SEASONAL  ZEROS 

Distinct98
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.030105
Minimum0
Maximum100
Zeros1036
Zeros (%)5.9%
Memory size273.0 KiB
2024-05-09T10:10:46.002965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median25
Q333
95-th percentile87
Maximum100
Range100
Interquartile range (IQR)21

Descriptive statistics

Standard deviation22.450243
Coefficient of variation (CV)0.77334349
Kurtosis1.4704315
Mean29.030105
Median Absolute Deviation (MAD)12
Skewness1.4343174
Sum507214
Variance504.0134
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.999463845 × 10-26
2024-05-09T10:10:46.234000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-05-09T10:10:46.932642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2024-05-09T10:10:47.135802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
25 6233
35.7%
12 3396
19.4%
37 1048
 
6.0%
0 1036
 
5.9%
87 1003
 
5.7%
50 651
 
3.7%
68 420
 
2.4%
20 377
 
2.2%
56 277
 
1.6%
62 212
 
1.2%
Other values (88) 2819
16.1%
ValueCountFrequency (%)
0 1036
5.9%
1 33
 
0.2%
2 39
 
0.2%
3 20
 
0.1%
4 82
 
0.5%
5 22
 
0.1%
6 28
 
0.2%
7 16
 
0.1%
8 83
 
0.5%
9 21
 
0.1%
ValueCountFrequency (%)
100 124
0.7%
98 1
 
< 0.1%
97 2
 
< 0.1%
96 2
 
< 0.1%
95 2
 
< 0.1%
94 4
 
< 0.1%
93 57
0.3%
92 4
 
< 0.1%
91 4
 
< 0.1%
90 3
 
< 0.1%
2024-05-09T10:10:46.602746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

dewpt
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct374
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.915911
Minimum-17.1
Maximum26.7
Zeros34
Zeros (%)0.2%
Memory size273.0 KiB
2024-05-09T10:10:47.466096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-17.1
5-th percentile-0.4
Q16.8
median12
Q317.9
95-th percentile22.5
Maximum26.7
Range43.8
Interquartile range (IQR)11.1

Descriptive statistics

Standard deviation7.1647674
Coefficient of variation (CV)0.60127735
Kurtosis-0.627254
Mean11.915911
Median Absolute Deviation (MAD)5.6
Skewness-0.30010491
Sum208194.8
Variance51.333893
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.001236149463
2024-05-09T10:10:47.687188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-05-09T10:10:48.506372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2024-05-09T10:10:48.668685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
18.9 111
 
0.6%
11.3 109
 
0.6%
9.3 109
 
0.6%
20 105
 
0.6%
18.4 103
 
0.6%
11.4 102
 
0.6%
9.4 102
 
0.6%
18.2 101
 
0.6%
8 101
 
0.6%
10.6 100
 
0.6%
Other values (364) 16429
94.0%
ValueCountFrequency (%)
-17.1 1
< 0.1%
-16.6 1
< 0.1%
-15.1 1
< 0.1%
-14.8 1
< 0.1%
-14.6 1
< 0.1%
-13.7 1
< 0.1%
-13.3 1
< 0.1%
-13.1 1
< 0.1%
-13 1
< 0.1%
-12.9 1
< 0.1%
ValueCountFrequency (%)
26.7 1
 
< 0.1%
26.5 1
 
< 0.1%
26.3 1
 
< 0.1%
26.2 1
 
< 0.1%
25.9 1
 
< 0.1%
25.8 5
< 0.1%
25.7 1
 
< 0.1%
25.6 1
 
< 0.1%
25.5 7
< 0.1%
25.4 5
< 0.1%
2024-05-09T10:10:48.150265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

rh
Numeric time series

NON STATIONARY  SEASONAL 

Distinct88
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.606685
Minimum10
Maximum98
Zeros0
Zeros (%)0.0%
Memory size273.0 KiB
2024-05-09T10:10:48.969286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile44
Q160
median70
Q379
95-th percentile88
Maximum98
Range88
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.602779
Coefficient of variation (CV)0.19827192
Kurtosis0.17172151
Mean68.606685
Median Absolute Deviation (MAD)9
Skewness-0.6054697
Sum1198696
Variance185.03561
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.056967976 × 10-21
2024-05-09T10:10:49.407760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-05-09T10:10:50.086177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2024-05-09T10:10:50.269347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
72 567
 
3.2%
73 549
 
3.1%
75 537
 
3.1%
69 520
 
3.0%
67 498
 
2.9%
68 496
 
2.8%
76 496
 
2.8%
66 490
 
2.8%
78 484
 
2.8%
71 479
 
2.7%
Other values (78) 12356
70.7%
ValueCountFrequency (%)
10 2
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
13 2
 
< 0.1%
14 4
< 0.1%
15 1
 
< 0.1%
16 3
< 0.1%
17 5
< 0.1%
18 2
 
< 0.1%
19 5
< 0.1%
ValueCountFrequency (%)
98 3
 
< 0.1%
96 12
 
0.1%
95 15
 
0.1%
94 39
 
0.2%
93 52
 
0.3%
92 87
 
0.5%
91 129
0.7%
90 155
0.9%
89 260
1.5%
88 260
1.5%
2024-05-09T10:10:49.767279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

Interactions

2024-05-09T10:10:30.254453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:18.800297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:21.743329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:23.119378image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:24.775433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:26.252345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:27.668366image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:28.950712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:30.656188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:19.266122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:22.036674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:23.286538image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:24.919488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:26.422280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:27.815841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:29.087012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:30.882269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:19.938583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:22.235772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:23.635558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:25.054758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:26.588953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:27.960360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:29.318112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:31.048769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:20.344913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:22.406510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:23.804650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:25.270311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:26.737683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:28.085432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:29.486162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:31.186003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:20.692064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:22.560023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:24.003155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:25.519631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:26.920219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:28.218256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:29.636036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:31.332017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:20.948801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:22.706580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:24.202341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:25.705269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:27.187023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:28.333824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:29.758399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:31.479903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:21.153520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:22.853423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:24.372903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:25.886772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:27.376883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:28.608334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:29.883184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:31.609477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:21.368668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:22.987365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:24.519722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:26.073289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:27.530475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:28.799034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T10:10:30.032127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-05-09T10:10:50.433340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
cloudsdewptdhidnighirhtempwind_spd
clouds1.0000.035-0.021-0.021-0.0220.235-0.0650.022
dewpt0.0351.0000.1920.1860.1990.3160.893-0.109
dhi-0.0210.1921.0000.9990.999-0.4040.3850.330
dni-0.0210.1860.9991.0000.999-0.4040.3800.330
ghi-0.0220.1990.9990.9991.000-0.4030.3920.328
rh0.2350.316-0.404-0.404-0.4031.000-0.106-0.254
temp-0.0650.8930.3850.3800.392-0.1061.000-0.001
wind_spd0.022-0.1090.3300.3300.328-0.254-0.0011.000

Missing values

2024-05-09T10:10:31.817365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-09T10:10:32.249713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

timestamp_localtempwind_spddhighidnicloudsdewptrh
2022-01-01 00:00:002022-01-01 00:00:0010.71.0000879.592
2022-01-01 01:00:002022-01-01 01:00:0010.72.6000628.888
2022-01-01 02:00:002022-01-01 02:00:0010.72.6000879.089
2022-01-01 03:00:002022-01-01 03:00:0010.22.6000688.589
2022-01-01 04:00:002022-01-01 04:00:0010.51.5000759.392
2022-01-01 05:00:002022-01-01 05:00:0010.51.5000878.487
2022-01-01 06:00:002022-01-01 06:00:0010.52.6000629.694
2022-01-01 07:00:002022-01-01 07:00:0010.01.0000379.194
2022-01-01 08:00:002022-01-01 08:00:008.01.5000566.590
2022-01-01 09:00:002022-01-01 09:00:008.61.03964340877.794
timestamp_localtempwind_spddhighidnicloudsdewptrh
2023-12-30 14:00:002023-12-30 14:00:0015.22.1087382745337.058
2023-12-30 15:00:002023-12-30 15:00:0014.23.6079298686358.870
2023-12-30 16:00:002023-12-30 16:00:0014.03.6062176559379.072
2023-12-30 17:00:002023-12-30 17:00:0013.24.09324125909.377
2023-12-30 18:00:002023-12-30 18:00:0012.23.6000008.980
2023-12-30 19:00:002023-12-30 19:00:0011.22.1000007.578
2023-12-30 20:00:002023-12-30 20:00:0010.72.6000006.575
2023-12-30 21:00:002023-12-30 21:00:0010.73.1000006.676
2023-12-30 22:00:002023-12-30 22:00:0010.64.5900006.676
2023-12-30 23:00:002023-12-30 23:00:007.71.0000005.385